Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels

TL;DR

This paper introduces a local, deterministic, parameter-free algorithm that efficiently detects overlapping communities and their hierarchy in weighted networks by analytically calculating resolution levels, outperforming existing methods in accuracy and speed.

Contribution

The paper presents a novel analytic, local community detection algorithm that reveals hierarchical structures without parameter tuning, improving accuracy and computational efficiency over prior methods.

Findings

More precise results on high-overlap LFR benchmarks

Comparable performance to GCE in real-world network analysis

Faster and more exact than numerical resolution level testing

Abstract

We propose a new local, deterministic and parameter-free algorithm that detects fuzzy and crisp overlapping communities in a weighted network and simultaneously reveals their hierarchy. Using a local fitness function, the algorithm greedily expands natural communities of seeds until the whole graph is covered. The hierarchy of communities is obtained analytically by calculating resolution levels at which communities grow rather than numerically by testing different resolution levels. This analytic procedure is not only more exact than its numerical alternatives such as LFM and GCE but also much faster. Critical resolution levels can be identified by searching for intervals in which large changes of the resolution do not lead to growth of communities. We tested our algorithm on benchmark graphs and on a network of 492 papers in information science. Combined with a specific post-processing, the algorithm gives much more precise results on LFR benchmarks with high overlap compared to other algorithms and performs very similar to GCE.